Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years.

This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal.

It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity.

The date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year.

Increases (decreases) are seen in areas of low (high) human population.

Recent decreases only partially offset a general post-drought increase in Sahelian woody cover.

Woody plants play a major role for the resilience of drylands and in peoples’ livelihoods. However, due to their scattered distribution, quantifying and monitoring woody cover over space and time is challenging. We develop a phenology driven model and train/validate MODIS (MCD43A4, 500 m) derived metrics with 178 ground observations from Niger, Senegal and Mali to estimate woody cover trends from 2000 to 2014 over the entire Sahel at 500 m scale.

Over the 15 year period we observed an average increase of 1.7 (± 5.0) woody cover (%) with large spatial differences: No clear change can be observed in densely populated areas (0.2 ± 4.2), whereas a positive change is seen in sparsely populated areas (2.1 ± 5.2). Woody cover is generally stable in cropland areas (0.9 ± 4.6), reflecting the protective management of parkland trees by the farmers. Positive changes are observed in savannas (2.5 ± 5.4) and woodland areas (3.9 ± 7.3).

The major pattern of woody cover change reveals strong increases in the sparsely populated Sahel zones of eastern Senegal, western Mali and central Chad, but a decreasing trend is observed in the densely populated western parts of Senegal, northern Nigeria, Sudan and southwestern Niger. This decrease is often local and limited to woodlands, being an indication of ongoing expansion of cultivated areas and selective logging.

We show that an overall positive trend is found in areas of low anthropogenic pressure demonstrating the potential of these ecosystems to provide services such as carbon storage, if not over-utilized. Taken together, our results provide an unprecedented synthesis of woody cover dynamics in the Sahel, and point to land use and human population density as important drivers, however only partially and locally offsetting a general post-drought increase.

Monitoring long-term biomass dynamics in drylands is of great importance for many environmental applications including land degradation and global carbon cycle modeling. Biomass has extensively been estimated based on the normalized difference vegetation index (NDVI) as a measure of the vegetation greenness. The vegetation optical depth (VOD) derived from satellite passive microwave observations is mainly sensitive to the water content in total aboveground vegetation layer. VOD therefore provides a complementary data source to NDVI for monitoring biomass dynamics in drylands, yet further evaluations based on ground measurements are needed for an improved understanding of the potential advantages.

In this study, we assess the capability of a long-term VOD dataset (1992–2011) to capture the temporal and spatial variability of in situ measured green biomass (herbaceous mass and woody plant foliage mass) in the semi-arid Senegalese Sahel.

Results show that the magnitude and peak time of VOD are sensitive to the woody plant foliage whereas NDVI seasonality is primarily governed by the green herbaceous vegetation stratum in the study area. Moreover, VOD is found to be more robust against typical NDVI drawbacks of saturation effect and dependence on plant structure (herbaceous and woody compositions) across the study area when used as a proxy for vegetation productivity. Finally, both VOD and NDVI well reflect the spatial and inter-annual dynamics of the in situ green biomass data; however, the seasonal metrics showing the highest degree of explained variance differ between the two data sources. While the observations in October (period of in situ data collection) perform best for VOD (r2 = 0.88), the small growing season integral (sensitive to recurrent vegetation) have the highest correlations for NDVI (r2 = 0.90).

Overall, in spite of the coarse resolution of 25 km, the study shows that VOD is an efficient proxy for estimating biomass of the entire vegetation stratum in the semi-arid Sahel and likely also in other dryland areas.

Trees, shrubs and bushes are an important element of savanna ecosystems and for livelihoods in dryland areas dependent on fuel–wood supply. During the past decades, several studies have seriously questioned prevailing narratives of a widespread and Sahel-wide decrease in woody cover, commending the relevance of large scale woody cover monitoring systems.

Most studies estimating tree canopy cover with remote sensing rely on high resolution imagery which allow direct mapping at a scale recognizing trees of a certain size as objects. However, imageries with a spatial resolution of 1–5 m are cumbersome to process, expensive, susceptible to clouds, and do only provide a static situation for a limited spatial area. Moreover, considering trees as objects, smaller isolated woody plant are missed. Moreover, the reliability of global tree cover products in semi-arid regions with open tree cover is contested.

We suggest an approach driven by vegetation phenology including in situ measured woody cover data across the Sahel and seasonal metrics from time series of MODIS and SPOT-VGT. The method is an indirect estimation of the canopy cover of all woody phanerophytes including trees, shrubs and bushes, and is based on the significant difference in phenophases of woody plants as compared to that of the herbaceous plants. In the Sahel, annual herbaceous plants are only green during the rainy season from June to October and senescence occurs after flowering in September towards the last rain events of the season. The leafing of most trees and shrubs is longer, with several evergreen species, and many woody species green-up ahead of the rains during the last month of the dry season, while annual herbaceous are dependent on the first rains to germinate.

Figure from Brandt et al., 2016: Seasonal distribution of woody leaf mass depending on the phenological type, modeled within the STEP primary production simulation model (Mougin, Lo Seen, Rambal, Gaston, & Hiernaux, 1995). The months of the wet season during which herbaceous grow are highlighted in a shaded box. Illustrations of typical herbaceous growing curves can be found in Mougin et al. (2014).

We tested 10 metrics representing the annual FAPAR dynamics for their ability to reproduce in situ woody cover at 43 sites (163 observations between 1993 and 2013). Both multi-year field data and satellite metrics are averaged to produce a steady map. Multiple regression models using the integral of FAPAR from the onset of the dry season to the onset of the rainy season, the start date of the growing season and the rate of decrease of the FAPAR curve achieve a cross validated r2 /RMSE (in % woody cover) of 0.73/3.0 (MODIS) and 0.70/3.2 (VGT). The extrapolation to Sahel scale shows an almost nine times higher woody cover than in the global tree cover product MOD44B which only captures trees of a certain minimum size. The derived woody cover map of the Sahel is made publicly available and represents an improvement of existing products and a contribution for future studies of drylands quantifying carbon stocks, climate change assessment, as well as parametrization of vegetation dynamic models.

Livestock farming constitutes the most widespread human activity and the dominant land use in rangeland ecosystems. At a global scale, it contributes 40% of the agricultural gross domestic product, and provides income for more than 1.3 billion people and nourishment for at least 800 million food-insecure people. In particular for the West African Sahel, livestock constitutes the first renewable resource and is mainly characterized by an extensive use of pastures in rangelands.

Since 1987 the Centre de Suivi Ecologique (CSE) operationally estimates the total annual biomass in Senegal in order to monitor the fodder availability of the national pastoral rangelands. Field data is collected along 1 km transects at 24 sites at the end of the wet season. Here, herbaceous and woody leaf biomass is measured and summed to the total available fodder biomass. This is done each year since 1987.

Using a linear regression with satellite images, the field data is projected to whole Senegal and gives stakeholders an estimation on the quantity and distribution of fodder biomass. Between 1987 and 1999, this method was implemented using the seasonal integrated NDVI (i.e., seasonal weighted average) from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA) satellites acquired in Local Area Coverage (LAC) format at the CSE receiving station in Dakar. Since 2000, the 1-km SPOT-VEGETATION NDVI have been used.

In this context, we developed a new operational system for monitoring total fodder biomass, including both herbaceous and woody leaf biomass. The proposed method is based on multiple linear regression models using phenological variables derived from the seasonal dynamics of the FAPAR SPOT-VEGETATION time series and ground measurements of total biomass production collected in different Sahelian ecosystems in Senegal over 15 years.

A model with three variables – large seasonal integral (LINTG), length of growing season and end of season decreasing rate – performed best (MAE = 605 kg DM/ha; R² = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999-2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg DM/ha; R² = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area using metrics related to ecosystem properties increased overall accuracy (MAE = 489.21 kg DM/ha; R² = 0.77). LINTG was the main explanatory variable for woody rangelands, whereas for areas dominated by herbaceous vegetation it was the PEAK metric. The proposed approach outperformed the established single-predictor model (MAE = 818 kg DM/ha and R² = 0.51) and should improve the operational monitoring of forage resources in Sahelian rangelands.

In the future, such early warning models should enable stakeholders to take decisions as early as September (current year as biomass shortage) with regard to livestock by triggering protocols designed for livestock management (e.g., Opération de Sauvegarde du Bétail ) in Senegal.

Land degradation mechanisms are related to two main categories, one related to climate change and one associated with local human impact, mostly land use change such as expansion of cultivation, agricultural intensification, overgrazing and overuse of woody vegetation. Land degradation characteristics, triggers and human influence are manifold and interrelated. Some of the indicators can be monitored using Earth Observation techniques (underlined in red):

During the last four decades, the Sahel was affected by below-normal precipitation with two severe drought periods in 1972–73 and in 1983–84. Because of this negative climate trend, many studies prioritized the Sahel “crisis” in terms of productivity loss and land degradation. These negative perceptions have been opposed with recent findings of improved greenness mostly in relation to recent improvement in rainfall.

The assessment of land degradation and quantifying its effects on land productivity have been both a scientific and political challenge. After four decades of Earth Observation applications, little agreement has been gained on the magnitude and direction of land degradation in the Sahel. The number of Earth Observation datasets and methods, biophysical and social drivers and the complexity of interactions make it difficult to apply aggregated Earth Observation indices for these non-linear processes. Hence, while many studies stress that the Sahel is greening, others indicate no trend or browning. The different generations of satellite sensors, the granularity of studies, the study period, the applied indices and the assumptions and/or computational methods impact these trends.

While there is a clearly positive trend in biomass production at Sahel scale, a loss in biodiversity and locally encroaching barren land are observed at the same time. Multi-scale Earth Observation analyses show that neither the desertification nor the greening paradigms can be generalized, as both attempt to simplify a very complex reality. Heterogeneity is an issue of scale, and very coarse-scaled vegetation trend analyses reveal a greening Sahel. However, locally-scaled studies are not uniform, observing greening and degradation at the same time.

We suggest several improvements: (1) harmonize time-series data, (2) promote knowledge networks, (3) improve data-access, (4) fill data gaps, (5) agree on scales and assumptions, (6) set up a denser network of long-term fields-surveys and (7) consider local perceptions and social dynamics, as local people’s perception of land degradation/improvements often disagree with Earth Observation analyses.

Thus, to allow multiple perspectives and avoid erroneous interpretations caused by data quality/scale issues/generalizations, we recommend combining multiple data sources at multiple scales. Furthermore, we underline the relevance of field data and experience, and results achieved by remote sensing techniques should not be interpreted without contextual knowledge.

GIMMS3g NDVI is widely used to assess vegetation trends from local to global scale. And even though it is the best long term dataset available right now (July 2015), people should be aware that a serious error affects the dataset in semi arid areas, like the Sahel, which has severe impacts on trend analysis. The dry season values around 2004 suddenly drop, which is not happening in reality. It is definitely related to the sensor change from NOAA16 to NOAA17, and also the last change to NOAA18 has an impact on the time series. The good thing is that the wet season values should be usable. The developers are aware about this problem and a new version is on it’s way. To illustrate the issue, find attached the GIMMS3g NDVI (v0) averaged over the Sahel belt, note the drop at the sensor change (first blue line) and the recovery at the next sensor change:

To further illustrate how serious this problem affects trend analysis, the same area is shown in the VOD dataset, which is found to be reliable:

a rough overlay highlights the different directions of annual trends:

The problem can be solved by excluding the dry season and using the small integral, calculated in TIMESAT:

Making use of 27 years of ground measurements, we were able to find evidence of the role of trees and grass on the greening of the Senegalese Sahel. This was made possible by a close collaboration with our colleagues from the CSE, the Centre de Suivi Ecologique in Dakar. Moreover, woody species abundance data provided by Gray Tappan from 1983 shows changes in biodiversity over 30 years. We thus provide ground based evidences against the conventional view of irreversible degradation in the Sahel.

Keywords:

biodiversity;

biomass monitoring;

degradation;

greening;

Sahel;

vegetation change

Abstract

After a dry period with prolonged droughts in the 1970s and 1980s, recent scientific outcome suggests that the decades of abnormally dry conditions in the Sahel have been reversed by positive anomalies in rainfall. Various remote sensing studies observed a positive trend in vegetation greenness over the last decades which is known as the re-greening of the Sahel. However, little investment has been made in including long-term ground-based data collections to evaluate and better understand the biophysical mechanisms behind these findings. Thus, deductions on a possible increment in biomass remain speculative. Our aim is to bridge these gaps and give specifics on the biophysical background factors of the re-greening Sahel. Therefore, a trend analysis was applied on long time series (1987–2013) of satellite-based vegetation and rainfall data, as well as on ground-observations of leaf biomass of woody species, herb biomass, and woody species abundance in different ecosystems located in the Sahel zone of Senegal. We found that the positive trend observed in satellite vegetation time series (+36%) is caused by an increment of in situ measured biomass (+34%), which is highly controlled by precipitation (+40%). Whereas herb biomass shows large inter-annual fluctuations rather than a clear trend, leaf biomass of woody species has doubled within 27 years (+103%). This increase in woody biomass did not reflect on biodiversity with 11 of 16 woody species declining in abundance over the period. We conclude that the observed greening in the Senegalese Sahel is primarily related to an increasing tree cover that caused satellite-driven vegetation indices to increase with rainfall reversal.

Woody cover, species and land cover change over 44 years are analyzed.

Object-based classifications are applied with high resolution images of 1967 and 2011.

Climate and especially human impact have caused extensive changes.

Changes are not always negative and a variety of spatial variations are shown.

Abstract:

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer’s fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.